The Aristotle Classifier: Using the Whole Glycomic Profile To Indicate a Disease State
The Aristotle Classifier: Using the Whole Glycomic Profile To Indicate a Disease State
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DOI：
10.1021/acs.analchem.9b01606
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发表时间：
2019-09-03
影响因子：
7.4
通讯作者：
Desaire, Heather
中科院分区：
文献类型：
--
作者：
Hua, David;Patabandige, Milani Wijeweera;Desaire, Heather
可以说，整体并不仅仅是一堆，而是部分之外的整体。我们建立了一个分类器，它使用整个血糖谱来区分来自两个不同来源的样本，而不是局限于几个糖形式。这种方法依赖于使用数千个特征，与目前的策略截然不同，目前的策略忽略了大多数血糖谱，而是选择几个特征，甚至是单个特征，以捕捉样本类型的差异。分类器可以用来区分材料的来源；适用的来源可以是不同的动物物种、不同的蛋白质生产方法，或者最重要的是，不同的生物状态(疾病和健康)。该分类器可以用于任何形式的血糖数据，包括衍生的单糖、完整的多糖或糖肽。它利用了这样一个事实，即改变来源材料可以以许多微妙的方式导致糖链结构的变化：一些糖形式可以上调，一些糖形式可以下调，一些糖形式可能看起来没有变化，但它们的比例-相对于其他存在的形式-可以改变到可检测的程度。通过使用样品的整体糖链丰度以及糖链之间的相对比例对样品进行分类，“亚里士多德分类器”在捕捉潜在趋势方面比糖组学中使用的标准分类程序更有效，包括主成分分析(PCA)。它的表现也优于使用单一的、具有代表性的基于糖链的生物标记物来对样本进行分类的工作流程。我们描述了亚里士多德分类器，并提供了几个使用来自几个来源的血糖数据进行生物标记物研究和其他分类问题的应用的例子。
"The totality is not, as it were, a mere heap, but the whole is something besides the parts."-Aristotle. We built a classifier that uses the totality of the glycomic profile, not restricted to a few glycoforms, to differentiate samples from two different sources. This approach, which relies on using thousands of features, is a radical departure from current strategies, where most of the glycomic profile is ignored in favor of selecting a few features, or even a single feature, meant to capture the differences in sample types. The classifier can be used to differentiate the source of the material; applicable sources may be different species of animals, different protein production methods, or, most importantly, different biological states (disease vs healthy). The classifier can be used on glycomic data in any form, including derivatized monosaccharides, intact glycans, or glycopeptides. It takes advantage of the fact that changing the source material can cause a change in the glycomic profile in many subtle ways: some glycoforms can be upregulated, some downregulated, some may appear unchanged, yet their proportion-with respect to other forms present-can be altered to a detectable degree. By classifying samples using the entirety of their glycan abundances, along with the glycans' relative proportions to each other, the "Aristotle Classifier" is more effective at capturing the underlying trends than standard classification procedures used in glycomics, including PCA (principal components analysis). It also outperforms workflows where a single, representative glycomic-based biomarker is used to classify samples. We describe the Aristotle Classifier and provide several examples of its utility for biomarker studies and other classification problems using glycomic data from several sources.